Abstract:

In this study, it was aimed to determine how the efficiency of the Computerized Adaptive Classification Testing (CACT) changes according to classification criteria, item selection and ability estimation methods. For this purpose, a pool of 500 items, which is based on 3 PLM and informs at the arbitrary cut-point and around, has been generated; individual abilities have been generated using normal distribution (N(0,1)) for 3000 individuals and the item response patterns have been generated randomly in R software with the Monte Carlo simulation. As classification criteria, Sequential Probability Ratio Test (SPRT), Generalized Likelihood Ratio (GLR) and Confidence Interval (CI) methods; as ability estimation methods, Expected a Posteriori (EAP) and Weighted Likelihood Estimation (WLE) methods; and as item selection methods, Maximum Fisher Information (MFI) and Kullback-Leibler Information (KLI) methods on the basis of cut-point (CP) and estimated ability (EA) have been crossed and 48 conditions have been investigated. At the end of the CACT simulations in R, the mean values of Average Test Length (ATL), Average Classification Accuracy (ACA), correlation between the true thetas and estimated thetas (r), bias, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for 25 replications have been calculated. According to the results of the study, it has been observed that the GLR and the CI classification criteria perform better in terms of test efficiency, however the SPRT works better in terms of the measurement precision; test efficiency increases as the indifference region of classification criteria expands or the error value decreases; all classification criteria have considerably high level of the classification accuracy in all conditions. It has been concluded that both ability estimation methods have successful estimation results in terms of the correlation between true and estimated thetas (r); whereas the EAP relatively performs better in terms of the measurement precision; and all of the item selection methods work similarly to each other however the MFI-EA performs better for all conditions in terms of all dependent variables.

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